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Speech Enhancement Based on Bayesian Low-Rank and Sparse Decomposition of Multichannel Magnitude Spectrograms

机译:基于多通道幅度谱的贝叶斯低秩和稀疏分解的语音增强

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This paper presents a blind multichannel speech enhancement method that can deal with the time-varying layout of microphones and sound sources. Since nonnegative tensor factorization (NTF) separates a multichannel magnitude (or power) spectrogram into source spectrograms without phase information, it is robust against the time-varying mixing system. This method, however, requires prior information such as the spectral bases (templates) of each source spectrogram in advance. To solve this problem, we develop a Bayesian model called robust NTF (Bayesian RNTF) that decomposes a multichannel magnitude spectrogram into target speech and noise spectrograms based on their sparseness and low rankness. Bayesian RNTF is applied to the challenging task of speech enhancement for a microphone array distributed on a hose-shaped rescue robot. When the robot searches for victims under collapsed buildings, the layout of the microphones changes over time and some of them often fail to capture target speech. Our method robustly works under such situations, thanks to its characteristic of time-varying mixing system. Experiments using a 3-m hose-shaped rescue robot with eight microphones show that the proposed method outperforms conventional blind methods in enhancement performance by the signal-to-noise ratio of 1.03 dB.
机译:本文提出了一种盲多通道语音增强方法,可以处理麦克风和声源随时间变化的布局。由于非负张量因子分解(NTF)将多通道幅值(或功率)频谱图分离为没有相位信息的源频谱图,因此它对于时变混合系统具有较强的鲁棒性。但是,该方法需要事先提供先验信息,例如每个源谱图的谱库(模板)。为解决此问题,我们开发了一种称为鲁棒NTF(贝叶斯RNTF)的贝叶斯模型,该模型基于它们的稀疏性和低秩将多通道幅度谱图分解为目标语音和噪声谱图。贝叶斯RNTF被应用于语音增强的挑战性任务,该增强是针对分布在软管形救援机器人上的麦克风阵列的。当机器人在倒塌的建筑物下搜索受害者时,麦克风的布局会随着时间而变化,其中一些麦克风通常无法捕获目标语音。由于其时变混合系统的特性,我们的方法在这种情况下能可靠地工作。使用带有8个麦克风的3 m软管形救援机器人进行的实验表明,该方法在信噪比为1.03 dB方面优于传统的盲法。

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